Tuning parameter of kalman filter in a wheel inspection

ABSTRACT

A method for tuning a parameter of a Kalman filter in a wheel inspection for a vehicle. The method includes: associating wheel inspection data of the vehicle with locations of wheels in the vehicle; evaluating a wheel stable score of the vehicle based on the wheel inspection data and the association, wherein the wheel stable score indicates reliability of the wheel inspection; and tuning measurement error covariance of the Kalman filter according to the evaluated wheel stable score. By using the method, the parameter of the Kalman filter can be dynamically tuned to enable the Kalman filter to filter more accurately to ensure the correct result of the wheel inspection. In addition, an apparatus for tuning a parameter of a Kalman filter and a wheel inspection system for a vehicle are disclosed.

CROSS REFERENCE TO RELATED APPLICATION

The present invention claims priority under 35 U.S.C. 119 from ChineseApplication 201110032433.1, filed Jan. 30, 2011, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to wheel inspection technology for avehicle, and particularly, to a method and apparatus for tuning aparameter of a Kalman filter in a wheel inspection to remove noises inwheel inspection data more effectively.

2. Description of Related Art

For railway vehicles, especially for high speed railway vehicles, wheelsare very important and costive assets. Generally, each wheel costs about$10,000, and a rolling stock has about 100 wheels. Given this, the costof the wheels in one vehicle is very high. In addition, the wheelsdirectly impact the vehicle's speed, safety and comfort.

To minimize wheel failure and to avoid catastrophic events, railwayoperators are usually equipped with a wheel inspection system to monitorrelevant parameters of the wheels and to detect abnormal conditions ofthe wheels. In the existing wheel inspection systems, usually sensorsare installed on the rail and are used to measure the relevantparameters of the wheels. This wheel data is then provided to a statusinspection system to analyze whether the shape of the wheel is circular,whether the wheel is worn down, what the wheel diameter difference is,etc., to help the operators know the status of the wheels. In general,the detected relevant parameters of the wheel include a wheel profileand wheel diameter value.

It is well known that there exists noise in the wheel data measured bythe sensors, which would cause an error in the analysis result of thewheel data, and may make the analysis result meaningless or generatefalse alarms. Therefore, it is necessary to remove the noise in thewheel inspection data to ensure that the analysis result can indicatethe current status of the wheels accurately. Thus, Kalman filteringtechnology is often effectively used in the existing wheel inspectionsystem to remove the noise in the signals.

The basic idea of the Kalman filter is to calculate an estimation valueof the current status based on the estimation value of the previousstatus and the measurement value of the current status—It is a kind ofrecursive estimation. The operation of the Kalman filter includes twophases: prediction and update. In the prediction phase, the currentstatus is predicted based on the estimation value of the previousstatus. In the update phase, the prediction value obtained in theprediction phase is optimized based on the measurement value of thecurrent status to obtain the more accurate new estimation value.

In the prediction phase, the current status is predicted under formula(1):{circumflex over (x)}_(k) ⁻=Ax_(k−1)   (1)

where {circumflex over (x)}_(k) ⁻ represents the status prediction valuefor time k, A represents a status transition matrix, and x_(k−1)represents the status estimation value for time k−1. Thus the predictionvalue of the prediction estimation covariance for time k is:P _(k) ⁻ =AP _(k−1) A ^(T) +Q   (2)

where P_(k) ⁻ represents the prediction value of the predictionestimation covariance for time k and P_(k−1) represents the estimationvalue of the prediction estimation covariance for time k−1.

In the update phase, Kalman gain is calculated from formula (3):K _(k) =P _(k) ⁻(P _(k) ⁻ +R)⁻¹   (³)

where K_(k) represents the gain for time k, and R represents themeasurement error covariance and is a constant. Then, the statusprediction value for time k is updated under formula (4) to obtain thenew status estimation value:{circumflex over (x)} _(k) ={circumflex over (x)} _(k) ⁻ +K _(k)(z _(k)−{circumflex over (x)} _(k) ⁻)   (4)

where {circumflex over (x)}_(k) represents the status estimation valuefor time k, and z_(k) represents the status measurement value for timek. In addition, the prediction value of the prediction estimationcovariance is updated under formula (5) to obtain the new estimationvalue of the prediction estimation covariance:P _(k)=(I−K _(k))P _(k) ⁻  (5)

where P_(k) represents the estimation value of the prediction estimationcovariance for time k.

In the Kalman filter, the Kalman gain K_(k) is in fact a balance factorfor the prediction estimation covariance P_(k) and the measurement errorcovariance R. If the measurement error covariance R is close to 0, theKalman gain K_(k) is close to 1, and the updated status estimation value{circumflex over (x)}_(k) is close to the status measurement valuez_(k). If the prediction estimation covariance P_(k) is close to 0, theKalman gain K_(k) is also close to 0, and the updated status estimationvalue {circumflex over (x)}_(k) is close to the status prediction value{circumflex over (x)}_(k) ⁻.

In the use of the Kalman filter, the measurement error covariance R isusually unchanged. However, in practice, the measurement errorcovariance R cannot remain unchanged. For example, in the case that theweather condition is changed or the working time is long, the sensorsinstalled on the rail will be affected, leading to the measurement errorcovariance R being changed. Once the parameter of the Kalman filter isinappropriate, the signal noise remove effect will be decreased, easilyresulting in the wrong analysis result. Therefore, it is necessary toconsider the changes of the measurement error covariance R of the Kalmanfilter in the wheel inspection to make the estimation result of theKalman filter more accurate.

SUMMARY OF THE INVENTION

The present invention provides a method for tuning a parameter of aKalman filter in a wheel inspection for a vehicle, including:associating wheel inspection data of the vehicles with locations ofcorresponding wheels in the vehicle; evaluating a wheel stable score ofthe vehicle based on the wheel inspection data and the association,wherein the wheel stable score indicates reliability of the wheelinspection; and tuning a measurement error covariance of the Kalmanfilter according to the evaluated wheel stable score.

According to another aspect, the present invention provides an apparatusfor tuning a parameter of a Kalman filter in a wheel inspection for avehicle, including: an association module that associates wheelinspection data of the vehicles with locations of corresponding wheelsin the vehicle; an evaluation module that evaluates a wheel stable scoreof the vehicle based on the wheel inspection data and the association,wherein the wheel stable score indicates reliability of the wheelinspection; and a parameter tuning module that tunes a measurement errorcovariance of the Kalman filter according to the evaluated wheel stablescore.

According to another aspect of the present invention, a wheel inspectionsystem for a vehicle, including: a plurality of sensors that measureparameters of wheels of the vehicle; an apparatus for tuning a parameterof a Kalman filter in a wheel inspection for a vehicle, including: anassociation module that associates wheel inspection data of the vehicleswith locations of corresponding wheels in the vehicle; an evaluationmodule that evaluates a wheel stable score of the vehicle based on thewheel inspection data and the association, wherein the wheel stablescore indicates reliability of the wheel inspection; and a parametertuning module that tunes a measurement error covariance of the Kalmanfilter according to the evaluated wheel stable score; and a Kalmanfilter that detects statuses of the wheels according to the measuredparameters of the wheels.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic flow chart of a method for tuning a parameter of aKalman filter in a wheel inspection for a vehicle according to anembodiment of the invention;

FIG. 2 is a schematic flow chart of the step of associating wheelinspection data with locations of wheels in the vehicle in the method ofthe embodiment shown in FIG. 1;

FIG. 3 is a schematic flow chart of the step of evaluating a wheelstable score in the embodiment shown in FIG. 1;

FIG. 4 is a schematic block diagram of an apparatus for tuning aparameter of a Kalman filter in a wheel inspection for a vehicleaccording to an embodiment of the invention;

FIG. 5 is a schematic block diagram of axle/bogie/car stable scorecalculation unit in the apparatus of the embodiment shown in FIG. 4;

FIG. 6 is a schematic block diagram of a wheel inspection system for avehicle according to one embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

It is believed that the above and other objects, features and advantagesof the present invention will become more apparent from the followingdetailed description of the preferred embodiments of the presentinvention in conjunction with the drawings.

FIG. 1 is a schematic flow chart of a method for tuning a parameter of aKalman filter in a wheel inspection for a vehicle according to anembodiment of the invention. The embodiment will be described below indetail in conjunction with the figure.

The method of this embodiment is based on the following two points: 1)the wheels in the same car, bogie or axle of the vehicle should havesubstantially the same wheel diameter value and similar wear; and 2) ifthe measurement result of the sensor as a measurement apparatus is notstable, the measurement result of the sensor tends to be an error. Thus,the reliability of the wheel measurement performed by the sensor can beevaluated by comparing the difference of the wheel diameter values ofthe wheels in the same axle, bogie or car, thereby determining whetherthe measurement noise of the sensor has changed and further determiningwhether the parameter of the Kalman filter needs to be tuned.

In the following description, the railway vehicle is described as anexample. It is well known that the railway vehicle includes multiplecars. Each car has front and rear bogies, each bogie has front and rearaxles, and each axle has left and right wheels. Typically, one railwayvehicle has 12 cars, 24 bogies, 48 axles and 96 wheels.

As shown in FIG. 1, in step S101, the wheel inspection data of thevehicle is associated with the locations of the wheels in the vehicle.In this embodiment, the sensors arranged on the rails measure therelevant parameters of the wheels of the passing vehicle, e.g. the wheeldiameter values of the wheels, to obtain the wheel inspection data.Then, this wheel inspection data is associated with the locations of therespective wheels.

FIG. 2 shows a schematic flow chart of the associating step. As shown inFIG. 2, in step S201, the locations of the wheels in the vehicle areidentified. For example, the locations of the wheels can be identifiedfrom vehicle head to vehicle tail and from left to right towards thevehicle head. In this case, the locations of the wheels can be expressedas 1_L, 1_R, 2_L, 2_R, 3_L, 3_R . . . . Next, in step S205, the locationof at least one of the axle, bogie and car where the wheel is located inthe vehicle is determined. In the above example, according to thestructure of the vehicle, the locations of the axle, bogie and car wherethe wheels 1_L and 1_R are located are 1, 1, 1 respectively, thelocations of the axle, bogie and car where the wheels 2_L and 2_R arelocated are 2, 1, 1 respectively, and the locations of the axle, bogieand car where the wheels 3_L and 3_R are located are 3, 2, 1respectively. Then, in step S210, the association is established amongthe wheel inspection data, the locations of the wheels and the locationof the at least one of the axle, bogie and car where the wheel islocated. In this embodiment, an association table can be built torepresent the relationship between the wheel inspection data and thelocations of the wheels, as shown in Table 1:

TABLE 1 Location of Location of Location of Location of Wheel Wheel AxleBogie Car Inspection Data 1_L 1 1 1 841.1 1_R 1 1 1 841.3 2_L 2 1 1842.9 2_R 2 1 1 846 3_L 3 2 1 845.2 3_R 3 2 1 845.6 . . . . . . . . . .. .

Based on such association, the wheel inspection data from the sensorsare structuralized so as to provide a basis for the evaluation of thewheel stable score described later.

Returning to FIG. 1, in step S105, the wheel stable score of the vehicleis evaluated based on the wheel inspection data from the sensors and theassociation established in step S101, wherein the wheel stable scoreindicates the reliability of the wheel inspection. Referring to FIG. 3,the step of evaluating the wheel stable score is described in detailbelow.

As shown in FIG. 3, in step S301, at least one of an axle stable score,a bogie stable score and a car stable score of the wheels of the wholevehicle is calculated. The method for calculating the axle stable score,the method for calculating the bogie stable score and the method forcalculating the car stable score are described below, respectively. Inthe following description, the wheel diameter value of the wheel is usedas the wheel inspection data. Those skilled in the art can understandthat other parameter values measured by the sensors can also be used asthe wheel inspection data, such as wheel profile thickness, wheelprofile height, etc.

(I) Method for Calculating the Axle Stable Score

First, an average value of a coaxial wheel diameter difference betweenthe left and right wheels in the same axle is calculated. The coaxialwheel diameter difference refers to the difference between the wheeldiameter values of the wheels in the same axle. In this embodiment, theaverage value of the coaxial wheel diameter difference can be calculatedusing formula (6):

$\begin{matrix}{\overset{\_}{{Diameter}_{axle}} = {\sum\limits_{{i = 1},2,\ldots\mspace{11mu},N}\frac{{{Diameter}_{i\_ L} - {Diameter}_{i\_ R}}}{N}}} & (6)\end{matrix}$

where Diameter_(axle) represents the average value of the coaxial wheeldiameter difference, N represents the number of the axles, Diameter_(i)_(—) _(L) represents the wheel diameter value of the left wheel in thei^(th) axle, and Diameter_(i) _(—) _(R) represents the wheel diametervalue of the right wheel in the i^(th) axle. Then, a current coaxialwheel diameter difference deviation score is calculated based on thecalculated average value of the coaxial wheel diameter difference and apredetermined mean value and variance of a normal coaxial wheel diameterdifference distribution. Usually, the normal coaxial wheel diameterdifference distribution is a normal distribution whose mean value andvariance can be obtained in advance by means of Statistical Product andService Solutions (SPSS) training. In this embodiment, the currentcoaxial wheel diameter difference deviation score can be calculated fromformula (7):

$\begin{matrix}{{Index} = \frac{\overset{\_}{{Diameter}_{axle}} - \overset{\_}{D_{axle}}}{\sigma_{axle}}} & (7)\end{matrix}$

where Index represents the coaxial wheel diameter difference deviationscore, and D_(axle) and σ_(axle) represent the mean value and varianceof the normal coaxial wheel diameter difference distribution,respectively. Of course, those skilled in the art can understand thatthe coaxial wheel diameter difference deviation score can be calculatedusing other methods. Then, the axle stable score of the wheels isdetermined according to the calculated current coaxial wheel diameterdifference deviation score. If the coaxial wheel diameter differencedeviation score is less than a first threshold, the axle stable score isequal to 0. If the coaxial wheel diameter difference deviation score isgreater than or equal to the first threshold, the axle stable score isequal to the difference between the coaxial wheel diameter differencedeviation score and the constant 1, as indicated by formula (8):

$\begin{matrix}{{Index}_{axle} = \left\{ \begin{matrix}{0,} & {{{when}\mspace{14mu}{Index}} < {{first}\mspace{14mu}{threshold}}} \\{{{Index} - 1},} & {{{when}\mspace{14mu}{Index}} \geq {{first}\mspace{14mu}{threshold}}}\end{matrix} \right.} & (8)\end{matrix}$

where Index_(axle) represents the axle stable score. The first thresholdcan be predetermined by a user and is usually greater than 1.

(II) Method for Calculating the Bogie Stable Score

First, a wheel diameter difference between the left wheels and a wheeldiameter difference between the right wheels in two axles in the samebogie are calculated, i.e., |Diameter_(i) _(—) _(L)−Diameter_(i+1) _(—)_(L)| and |Diameter_(i) _(—R) −Diameter_(i+1) _(—R) |, as a co-bogiewheel diameter difference. Then, an average value of the co-bogie wheeldiameter difference of all bogies is calculated, as indicated by formula(9):

$\begin{matrix}{\overset{\_}{{Diameter}_{bogie}} = {\sum\limits_{{i = 1},3,5,\ldots\mspace{11mu},{({{N/2} - 1})}}\frac{\begin{matrix}{{{{Diameter}_{i\_ L} - {Diameter}_{i + {1{\_ L}}}}} +} \\{{{Diameter}_{i\_ R} - {Diameter}_{i + {1{\_ R}}}}}\end{matrix}}{N/2}}} & (9)\end{matrix}$

where Diameter_(bogie) represents the average value of the co-bogiewheel diameter difference. Then a current co-bogie wheel diameterdifference deviation score is calculated based on the calculated averagevalue of the co-bogie wheel diameter difference and a predetermined meanvalue and variance of a normal co-bogie wheel diameter differencedistribution. Usually, the normal co-bogie wheel diameter differencedistribution is a normal distribution whose mean value and variance canbe obtained by means of Statistical Product and Service Solutions (SPSS)training. In this embodiment, the co-bogie wheel diameter differencedeviation score can be calculated from formula (10):

$\begin{matrix}{{Index} = \frac{\overset{\_}{{Diameter}_{bogie}} - \overset{\_}{D_{bogie}}}{\sigma_{bogie}}} & (10)\end{matrix}$

where Index represents the co-bogie wheel diameter difference deviationscore, and D_(bogie) and σ_(bogie) represent the mean value and varianceof the normal co-bogie wheel diameter difference distribution,respectively. Of course, those skilled in the art can understand thatthe co-bogie wheel diameter difference deviation score can be calculatedusing other methods. Then, the bogie stable score is determinedaccording to the calculated current co-bogie wheel diameter differencedeviation score. In this embodiment, if the co-bogie wheel diameterdifference deviation score is less than a second threshold, the bogiestable score is equal to 0. If the co-bogie wheel diameter differencedeviation score is greater than or equal to the second threshold, thebogie stable score is equal to the difference between the co-bogie wheeldiameter difference deviation score and the constant 1, as indicated byformula (11):

$\begin{matrix}{{Index}_{bogie} = \left\{ \begin{matrix}{0,} & {{{when}\mspace{14mu}{Index}} < {{second}\mspace{14mu}{threshold}}} \\{{{Index} - 1},} & {{{when}\mspace{14mu}{Index}} \geq {{second}\mspace{14mu}{threshold}}}\end{matrix} \right.} & (11)\end{matrix}$

where Index_(bogie) represents the bogie stable score. In addition, thesecond threshold can be predetermined by the user and is usually greaterthan 1.

In the above method for calculating the bogie stable score, the casewhere a bogie has two axles is described, but those skilled in the artcan understand that this method can also be applied, with slightmodifications, to the case where a bogie has more than two axles.

(III) Method for Calculating the Car Stable Score

First, average values of the wheel diameter values of the left and rightwheels in two bogies in the same car are calculated, as indicated byformulas (12) and (13):

$\begin{matrix}{\overset{\_}{{Diameter}_{{i\_ f}{\_ bogie}}} = \frac{\begin{matrix}{{Diameter}_{{8{({i - 1})}} + {1{\_ L}}} + {Diameter}_{{8{({i - 1})}} + {1{\_ R}}} +} \\{{Diameter}_{{8{({i - 1})}} + {2{\_ L}}} + {Diameter}_{{8{({i - 1})}} + {2{\_ R}}}}\end{matrix}}{4}} & (12) \\{\overset{\_}{{Diameter}_{{i\_ b}{\_ bogie}}} = \frac{\begin{matrix}{{Diameter}_{{8{({i - 1})}} + {3{\_ L}}} + {Diameter}_{{8{({i - 1})}} + {3{\_ R}}} +} \\{{Diameter}_{{8{({i - 1})}} + {4{\_ L}}} + {Diameter}_{{8{({i - 1})}} + {4{\_ R}}}}\end{matrix}}{4}} & (13)\end{matrix}$

where Diameter_(i) _(—) _(ƒ) _(—) _(bogie) represents the average valueof the wheel diameter values of the wheels in the front bogie in thei^(th) car, and Diameter_(i) _(—) _(b) _(—) _(bogie) represents theaverage value of the wheel diameter values of the wheels in the rearbogie in the i^(th) car. Next, the wheel diameter difference between thetwo bogies is calculated, i.e. | Diameter_(i) _(—) _(ƒ) _(—) _(bogie) −Diameter_(i) _(—) _(b) _(—) _(bogie) |, as a co-car wheel diameterdifference. Then, an average value of the co-car wheel diameterdifference of all cars is calculated, as indicated by formula (14):

$\begin{matrix}{\overset{\_}{{Diameter}_{car}} = {\sum\limits_{{i = 1},2,\ldots\mspace{11mu},{N/4}}\frac{{\overset{\_}{{Diameter}_{{i\_ f}{\_ bogie}}} - \overset{\_}{{Diameter}_{{i\_ b}{\_ bogie}}}}}{N/4}}} & (14)\end{matrix}$

Then, a current co-car wheel diameter difference deviation score iscalculated based on the calculated average value of the co-car wheeldiameter difference and a predetermined mean value and variance of anormal co-car wheel diameter difference distribution. Usually, thenormal co-car wheel diameter difference distribution under normalcircumstances is a normal distribution whose mean value and variance canbe obtained by means of Statistical Product and Service Solutions (SPSS)training. In this embodiment, the co-car wheel diameter differencedeviation score can be calculated from formula (15):

$\begin{matrix}{{Index} = \frac{\overset{\_}{{Diameter}_{car}} - \overset{\_}{D_{car}}}{\sigma_{car}}} & (15)\end{matrix}$

where Index represents the co-car wheel diameter difference deviationscore, and D_(car) and σ_(car) represent the mean value and variance ofthe normal co-car wheel diameter difference distribution, respectively.Of course, those skilled in the art can understand that the co-car wheeldiameter difference deviation score can be calculated using othermethods. Then, the car stable score is determined according to thecalculated current co-car wheel diameter difference deviation score. Inthis embodiment, if the co-car wheel diameter difference deviation scoreis less than a third threshold, the car stable score is equal to 0. Ifthe co-car wheel diameter difference deviation score is greater than orequal to the third threshold, the car stable score is equal to thedifference between the co-car wheel diameter difference deviation scoreand the constant 1, as indicated by formula (16):

$\begin{matrix}{{Index}_{car} = \left\{ \begin{matrix}{0,} & {{{when}\mspace{14mu}{Index}} < {{third}\mspace{14mu}{threshold}}} \\{{{Index} - 1},} & {{{when}\mspace{14mu}{Index}} \geq {{third}\mspace{14mu}{threshold}}}\end{matrix} \right.} & (16)\end{matrix}$

where Index_(car) represents the car stable score. The third thresholdcan be predetermined by the user and is usually greater than 1.

In the above method for calculating the car stable score, the case wherea car has two bogies is described, but those skilled in the art canunderstand that this method can also be applied, with slightmodifications, to the case where a car has more than two bogies.

Then, in step S305, the wheel stable score is calculated based on atleast one of the axle stable score, the bogie stable score and the carstable score calculated in step S301. In this embodiment, the wheelstable score is calculated as a weight sum of the axle stable score, thebogie stable score and the car stable score, wherein each of the weightsof the respective stable scores is not less than 0 and not greater than1, and the sum of the weights is equal to 1, as indicated by formula(17):StableIndex=w ₁Index_(axle) +w ₂Index_(bogie) +w ₃Index_(car)   (17)

where StableIndex represents the wheel stable score, and w₁, w₂, w₃represent the weights of the axle stable score Index_(axle), the bogiestable score Index_(bogie) and the car stable score Index_(car),respectively.

If any one of the axle stable score, the bogie stable score and the carstable score is calculated, the weight of the calculated stable score isset to 1 and the weights of the other stable scores are set to 0. If anytwo of the axle stable score, the bogie stable score and the car stablescore are calculated, the weight of the non-calculated stable score isset to 0.

Back again to FIG. 1, in step S110, the measurement error covariance ofthe Kalman filter is tuned based on the wheel stable score evaluated instep S105. As mentioned above, if the measurement error covariance ofthe Kalman filter always remains unchanged, it might lead to inaccuratefiltering of the Kalman filter; therefore it is necessary to tune themeasurement error covariance.

In this embodiment, first, the evaluated wheel stable score is comparedwith a predetermined stable threshold. The stable threshold, as an upperlimit of the wheel stable score, can be preset and stored by the user.If the wheel stable score is less than the stable threshold, whichindicates that the measurement of the wheel parameters by the sensors isnormal, then the measurement error covariance of the Kalman filtercannot be tuned. If the wheel stable score is greater than or equal tothe stable threshold, the measurement error covariance would be tuned.

Further, in order to avoid frequently tuning the measurement errorcovariance, it is further determined, when the comparison result is thatthe wheel stable score is greater than or equal to the stable threshold,whether the times of consecutive occurrences of the wheel stable scorebeing greater than or equal to the stable threshold exceeds apredetermined number of times. If the number of times of the consecutiveoccurrences exceeds the predetermined number of times, the measurementerror covariance would be tuned. If the number of times of consecutiveoccurrences is less than the predetermined number of times, themeasurement error covariance is not tuned. Assuming that thepredetermined number of times is set to M, then the times of theconsecutive occurrences exceeding the predetermined times means that M−1wheel stable scores obtained in the previous M−1 measurements beforethis measurement are greater than or equal to the stable threshold.

In fact, the measurement error covariance and the wheel stable scoresatisfy a linear relationship, namelyR=A(ƒ(StableIndex)−ƒ(StableIndex_(threshold)))+R ₀   (18)

where R represents the tuned measurement error covariance, A is aconstant, ƒ(·) represents a linear function, StableIndex represents thecalculated wheel stable score, StableIndex_(threshold) represents thestable threshold, and R₀ represents the initial measurement errorcovariance. The constant A and the linear function ƒ(·) can be obtainedby training and vary with the vehicle.

The initial measurement error covariance R₀ can be calculated asfollows. The wheel diameter measurement value during wheel truing isselected as a wheel diameter measurement true value, including ameasurement true value before the wheel truing and a measurement truevalue after the wheel truing. The measurement data before and afterwheel truing measured by the sensors at the time nearest to the wheeltruing are selected, and the difference between the measurement data andthe measurement true value is calculated as a measurement error. Thenthe initial measurement error covariance R₀ can be obtained according toa wheel diameter difference training model.

Therefore, the measurement error covariance can be tuned according tothe above formula (18).

Although the railway vehicle is described as an example in thisembodiment, those skilled in the art can understand that the method ofthe embodiment can also be applied to other kind of vehicles withsimilar structures.

It can be seen from the above description that the method for tuning aparameter of a Kalman filter in wheel inspection for a vehicle of thisembodiment dynamically tunes the measurement error covariance of theKalman filter by utilizing the vehicle's structure features, associatingthe wheel inspection data with the locations of the wheels, andevaluating the wheel stable score of the vehicle, so as to make theanalysis result of the Kalman filter more accurate. In addition, themethod of the embodiment is easy to implement.

Under the same inventive concept, FIG. 4 shows a schematic block diagramof an apparatus 400 for tuning a parameter of a Kalman filter in a wheelinspection for a vehicle according to an embodiment of the presentinvention. The embodiment will be described below in detail inconjunction with the figure, wherein for the same portions as those inthe previous embodiment, the description thereof is omitted properly.

As shown in FIG. 4, the apparatus 400 for tuning a parameter of a Kalmanfilter in a wheel inspection for a vehicle of this embodiment includes:association module 401 which associates the wheel inspection data of thevehicle with the locations of the wheels in the vehicle; evaluationmodule 402 which evaluates the wheel stable score of the vehicle basedon the wheel inspection data and the association established in theassociation module 401, wherein the wheel stable score indicates thereliability of the wheel inspection; and parameter tuning module 403which tunes the measurement error covariance of the Kalman filteraccording to the evaluated wheel stable score.

The parameters of the wheels (such as the wheel diameter value) measuredby the sensors are provided to the apparatus 400 as the wheel inspectiondata. First, the associations between the wheel inspection data and thelocations of the wheels are established in the association module 401.In the association module 401, wheel identification unit 4011 identifiesthe locations of the wheels in the vehicle, for example, the locationsof the wheels can be identified as 1_L, 1_R, 2_L, 2_R, 3_L, 3_R . . . .Then, in location determination unit 4012, the location of at least oneof the axle, bogie and car where the wheel is located in the vehicle isdetermined, and in association establishment unit 4013, the associationis established among the wheel inspection data, the locations of thewheels and the location of at least one of the axle, bogie and car wherethe wheel is located.

The wheel inspection data structuralized by the association module 401are provided to the evaluation module 402 to evaluate the wheel stablescore of the vehicle so as to determine the reliability of this wheelinspection. In the evaluation module 402, axle/bogie/car stable scorecalculation unit 4021 calculates at least one of the axle stable score,the bogie stable score and the car stable score of the wheels.

FIG. 5 shows a schematic block diagram of the axle/bogie/car stablescore calculation unit 4021. As shown in FIG. 5, the axle/bogie/carstable score calculation unit 4021 includes wheel inspection datadifference calculation unit 501, wheel inspection data differencedeviation score calculation unit 502 and stable score determination unit503. In the following description, the wheel diameter value of the wheelis used as the wheel inspection data. Those skilled in the art canunderstand that other parameter values measured by the sensors can alsobe used as the wheel inspection data.

In the calculation of the axle stable score, first, wheel inspectiondata difference calculation unit 501 calculates the average value of thecoaxial wheel diameter difference between the left and right wheels inthe same axle, for example, according to the foregoing formula (6).Then, wheel inspection data difference deviation score calculation unit502 calculates the current coaxial wheel diameter difference deviationscore based on the average value of the coaxial wheel diameterdifference and the predetermined mean value and variance of the normalcoaxial wheel diameter difference distribution, for example, accordingto the foregoing formula (7). Finally, stable score determination unit503 determines the axle stable score of the wheels according to thecalculated current coaxial wheel diameter difference deviation score,for example, according to the foregoing formula (8).

In the calculation of the bogie stable score, first, the wheelinspection data difference calculation unit 501 calculates the wheeldiameter difference between the left wheels and the wheel diameterdifference between the right wheels in two axles in the same bogie asthe co-bogie wheel diameter difference. It also calculates the averagevalue of the co-bogie wheel diameter difference of all bogies, forexample, according to the foregoing formula (9). Then, the wheelinspection data difference deviation score calculation unit 502calculates the current co-bogie wheel diameter difference deviationscore based on the calculated average value of the co-bogie wheeldiameter difference and the predetermined mean value and variance of thenormal co-bogie wheel diameter difference distribution, for example,according to the foregoing formula (10). Then, the stable scoredetermination unit 503 determines the bogie stable score of the wheelsaccording to the calculated current co-bogie wheel diameter differencedeviation score, for example, according to the foregoing formula (11).In the above calculation of the bogie stable score, the case where abogie has two axles is described, but those skilled in the art canunderstand that the related formulas can be applied to the case where abogie has more than two axles, with adaptive modifications.

In the calculation of the car stable score, first, the wheel inspectiondata difference calculation unit 501 calculates the average values ofthe wheel diameter values of the left and right wheels in two bogies inthe same car, for example, according to the foregoing formulas (12) and(13); and calculates the wheel diameter difference between the twobogies as the co-car wheel diameter difference. It then calculates theaverage value of the co-car wheel diameter difference of all cars, forexample, according to the foregoing formula (14). Then, the wheelinspection data difference deviation score calculation unit 502calculates the current co-car wheel diameter difference deviation scorebased on the calculated average value of the co-car wheel diameterdifference and the predetermined mean value and variance of the normalco-car wheel diameter difference distribution, for example, according tothe foregoing formula (15). Then, the stable score determination unit503 determines the car stable score of the wheels according to thecalculated current co-car wheel diameter difference deviation score, forexample, according to the foregoing formula (16). In the abovecalculation of the car stable score, the case where a car has two bogiesis described, but those skilled in the art can understand that therelated formulas can be applied to the case where a car has more thantwo bogies, with adaptive modifications.

Returning to FIG. 4, after at least one of the axle stable score, thebogie stable score and the car stable score is calculated by theaxle/bogie/car stable score calculation unit 4021, wheel stable scorecalculation unit 4022 calculates the wheel stable score using at leastone of the axle stable score, the bogie stable score and the car stablescore, for example, according to the foregoing formula (17). In thewheel stable score calculation unit 4022, the wheel stable score iscalculated as the weight sum of the axle stable score, the bogie stablescore and the car stable score, wherein each of the weights of therespective stable scores is not less than 0 and not greater than 1, andthe sum of the weights is equal to 1.

The wheel stable score evaluated by the evaluation module 402 isprovided to the parameter tuning module 403 to determine whether themeasurement error covariance of the Kalman filter needs to be tuned. Inthe parameter tuning module 403, comparison unit 4031 compares the wheelstable score with the predetermined stable threshold, and if the wheelstable score is greater than or equal to the stable threshold, tuningunit 4032 tunes the measurement error covariance.

Further, the parameter tuning module 403 can include determination unit4033. When the result of the comparison unit 4031 is that the wheelstable score is greater than or equal to the stable threshold, thedetermination unit 4033 determines whether the times of the consecutiveoccurrences of the case where the wheel stable score is greater than orequal to the stable threshold exceeds a predetermined number of times.If the number of times of consecutive occurrences exceeds thepredetermined number of times, the tuning unit 4032 tunes themeasurement error covariance. Thus, the measurement error covariancewould not need to be tuned frequently.

In this embodiment, the tuning unit 4032 tunes the measurement errorcovariance according to the foregoing formula (18).

It should be noted that the apparatus 400 for tuning a parameter of aKalman filter in wheel inspection for a vehicle of this embodiment canoperatively implement the method for tuning a parameter of a Kalmanfilter in wheel inspection for a vehicle as shown in FIGS. 1 to 3.

FIG. 6 shows a wheel inspection system for a vehicle according to oneembodiment of the present invention. The wheel inspection systemincludes: a plurality of sensors 601 which measure the parameters of thewheels of the vehicle; the apparatus 400 for tuning a parameter of aKalman filter in wheel inspection for a vehicle as shown in FIG. 4; anda Kalman filter 602 which detects the status of the wheels based on themeasured parameters of the wheels from the plurality of sensors.

The method disclosed in the above embodiments may be implemented inhardware, software, or combinations thereof. The hardware portion may beimplemented by dedicated logic. For example, the apparatus for tuning aparameter of a Kalman filter in a wheel inspection for a vehicle and itscomponents in the embodiment may be implemented by hardware circuitssuch as large scale Integrated circuits or gate arrays, semiconductorssuch as logic chips, transistors, or programmable hardware devices suchas field programmable gate arrays, programmable logic devices, etc., ormay be implemented by software which can be executed by variousprocessors, or may be implemented by the combination of the abovehardware circuits and software. The software portion can be stored in amemory and may be executed by an appropriate instruction executionsystem, such as microprocessor, personal computer (PC) or mainframe.

Although the method and apparatus for tuning a parameter of a Kalmanfilter in a wheel inspection for a vehicle of the present invention havebeen described in detail with some exemplary embodiments, theseembodiments are not exhaustive, and those skilled in the art can realizevarious changes and modifications within the spirit and scope of thepresent invention. Therefore, the present invention is not limited tothese embodiments, the scope of which is not limited by the appendedclaims.

The invention claimed is:
 1. A method for tuning a parameter of a Kalmanfilter in a wheel inspection for a vehicle, comprising: associatingwheel inspection data of said vehicles with locations of correspondingwheels in said vehicle; evaluating a wheel stable score of said vehiclebased on said wheel inspection data and said association, wherein saidwheel stable score indicates reliability of said wheel inspection, thewheel stable score calculated according to a wheel diameter differentialof left and right wheels coupled to a common axis; and dynamicallytuning a measurement error covariance (R) of said Kalman filteraccording to said evaluated wheel stable score.
 2. The method accordingto claim 1, wherein associating wheel inspection data of said vehiclewith locations of said corresponding wheels in said vehicle comprises:identifying said locations of said corresponding wheels in said vehicle;determining said location in said vehicle for at least one of an axle, abogie and a car where said wheel is located; and establishing anassociation among said wheel inspection data, the locations of saidwheels and the location of the at least one of said axle, said bogie andsaid car where said wheel is located.
 3. The method according to claim2, wherein evaluating a wheel stable score of said vehicle based on saidwheel inspection data and said association comprises: calculating atleast one of an axle stable score, a bogie stable score and a car stablescore of said wheels based on said association among said wheelinspection data, said locations of said wheels and said location of theat least one of said axle, said bogie and said car where said wheel islocated; and calculating said wheel stable score based on the at leastone of said axle stable score, said bogie stable score and said carstable score.
 4. The method according to claim 3, wherein calculatingsaid axle stable score of said wheels comprises: calculating an averagevalue of coaxial wheel inspection data differences between the left andright wheels in the respective same axles; calculating a current coaxialwheel inspection data difference deviation score based on said averagevalue of the coaxial wheel inspection data differences and a mean valueand a variance value of a predetermined normal distribution of coaxialwheel inspection data differences; and determining the axle stable scoreof the wheels according to the current coaxial wheel inspectional datadifference deviation score.
 5. The method according to claim 3, whereincalculating said bogie stable score of said wheels comprises:calculating, for each bogie, a wheel inspection data difference betweenthe left wheels in said bogie and a wheel inspection data differencebetween the right wheels in said bogie as a co-bogie wheel inspectiondata difference for said bogie; calculating an average value of saidco-bogie wheel inspection data differences; calculating a currentco-bogie wheel inspection data difference deviation score based on theaverage value of the co-bogie wheel inspection data differences and amean value and a variance value of a predetermined normal distributionof co-bogie wheel inspection data differences; and determining saidbogie stable score of said wheels according to said current co-bogiewheel inspection data difference deviation score.
 6. The methodaccording to claim 3, wherein calculating said car stable score of saidwheels comprises: calculating average values of said wheel inspectiondata of the left and right wheels in the respective bogies in the samecar, and calculating wheel inspection data differences between thebogies as co-car wheel inspection data differences; calculating anaverage value of said co-car wheel inspection data differences;calculating a current co-car wheel inspection data difference deviationscore based on the average value of the co-car wheel inspection datadifferences and a mean value and a variance value of a predeterminednormal distribution of co-car wheel inspection data differences; anddetermining said car stable score of said wheels according to saidcurrent co-car wheel inspection data difference deviation score.
 7. Themethod according to claim 3, wherein said wheel stable score iscalculated as a weight sum of said axle stable score, said bogie stablescore and said car stable score, wherein each of said weights of saidaxle stable score, said bogie stable score and the car stable score iswithin a range of 0 to 1, and the sum of said weights is equal to
 1. 8.The method according to claim 1, wherein tuning said measurement errorcovariance of said Kalman filter comprises: comparing said wheel stablescore with a predetermined stable threshold; and provided that the wheelstable score is not less than the stable threshold, tuning saidmeasurement error covariance.
 9. The method according to claim 8,wherein tuning said measurement error covariance of said Kalman filterfurther comprises: determining, provided that said wheel stable score isnot less than said stable threshold, whether a number of times ofconsecutive occurrences when said wheel stable score is not less thanthe stable threshold exceeds a predetermined value; and tuning saidmeasurement error covariance provided that said number of times ofconsecutive occurrences exceeds said predetermined value.
 10. The methodaccording to 9, wherein said measurement error covariance is tunedaccording to the following formula:R=A(ƒ(StableIndex)−ƒ(StableIndex_(threshold)))+R ₀   (18) whererepresents tuned measurement error covariance, A is a constant,represents a linear function, represents calculated wheel stable score,represents stable threshold, and represents initial measurement errorcovariance.
 11. A non-transitory computer readable article ofmanufacture tangibly embodying computer readable instructions which,when executed, cause a computer to carry out the steps of a method fortuning a parameter of a Kalman filter in a wheel inspection for avehicle, the method comprising: associating wheel inspection data ofsaid vehicle with locations of corresponding wheels in said vehicle;evaluating a wheel stable score of said vehicle based on said wheelinspection data and said association, wherein said wheel stable scoreindicates reliability of said wheel inspection, the wheel stable scorecalculated according to a wheel diameter differential of left and rightwheels coupled to a common axis; and dynamically tuning a measurementerror covariance (R) of said Kalman filter according to said evaluatedwheel stable score.